Overview

Dataset statistics

Number of variables18
Number of observations36008
Missing cells94400
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory144.0 B

Variable types

Numeric11
Text3
Unsupported2
Categorical1
DateTime1

Alerts

latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
room_type is highly imbalanced (58.4%)Imbalance
neighbourhood_group has 36008 (100.0%) missing valuesMissing
price has 1771 (4.9%) missing valuesMissing
last_review has 10302 (28.6%) missing valuesMissing
reviews_per_month has 10302 (28.6%) missing valuesMissing
license has 36008 (100.0%) missing valuesMissing
price is highly skewed (γ1 = 66.96707573)Skewed
minimum_nights is highly skewed (γ1 = 28.22092657)Skewed
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 10302 (28.6%) zerosZeros
availability_365 has 5475 (15.2%) zerosZeros
number_of_reviews_ltm has 14214 (39.5%) zerosZeros

Reproduction

Analysis started2024-04-27 15:57:43.905576
Analysis finished2024-04-27 15:58:30.662447
Duration46.76 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct36008
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4457301 × 1017
Minimum17878
Maximum1.0538233 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:31.332162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile2958870.7
Q128637937
median5.7613679 × 1017
Q38.7267609 × 1017
95-th percentile1.0398874 × 1018
Maximum1.0538233 × 1018
Range1.0538233 × 1018
Interquartile range (IQR)8.7267609 × 1017

Descriptive statistics

Standard deviation4.4185817 × 1017
Coefficient of variation (CV)0.99389336
Kurtosis-1.8169913
Mean4.4457301 × 1017
Median Absolute Deviation (MAD)4.7330968 × 1017
Skewness0.089169612
Sum-3.5887837 × 1018
Variance1.9523864 × 1035
MonotonicityNot monotonic
2024-04-27T12:58:31.886984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216461 1
 
< 0.1%
6.074455479 × 10171
 
< 0.1%
6.074073247 × 10171
 
< 0.1%
6.043327156 × 10171
 
< 0.1%
6.07416832 × 10171
 
< 0.1%
6.04350575 × 10171
 
< 0.1%
6.008074554 × 10171
 
< 0.1%
6.008473518 × 10171
 
< 0.1%
6.008841958 × 10171
 
< 0.1%
6.007433303 × 10171
 
< 0.1%
Other values (35998) 35998
> 99.9%
ValueCountFrequency (%)
17878 1
< 0.1%
25026 1
< 0.1%
35764 1
< 0.1%
41198 1
< 0.1%
48305 1
< 0.1%
48901 1
< 0.1%
49179 1
< 0.1%
50759 1
< 0.1%
51703 1
< 0.1%
53533 1
< 0.1%
ValueCountFrequency (%)
1.053823262 × 10181
< 0.1%
1.053808194 × 10181
< 0.1%
1.05378934 × 10181
< 0.1%
1.053756202 × 10181
< 0.1%
1.053746406 × 10181
< 0.1%
1.053743193 × 10181
< 0.1%
1.053736264 × 10181
< 0.1%
1.053720091 × 10181
< 0.1%
1.053716797 × 10181
< 0.1%
1.053711356 × 10181
< 0.1%

name
Text

Distinct14150
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:32.609559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length167
Median length92
Mean length62.758248
Min length31

Characters and Unicode

Total characters2259799
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10847 ?
Unique (%)30.1%

Sample

1st rowRental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath
2nd rowRental unit in Rio de Janeiro · 2 bedrooms · 5 beds · 3 baths
3rd rowRental unit in Rio de Janeiro · 1 bedroom · 2 beds · 2.5 baths
4th rowHome in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath
5th rowRental unit in Rio de Janeiro · 1 bedroom · 2 beds · 1 bath
ValueCountFrequency (%)
· 132069
23.3%
1 52893
 
9.4%
in 36008
 
6.4%
2 27550
 
4.9%
rental 26976
 
4.8%
unit 26976
 
4.8%
rio 25052
 
4.4%
de 24336
 
4.3%
janeiro 24223
 
4.3%
beds 23303
 
4.1%
Other values (494) 166266
29.4%
2024-04-27T12:58:33.669329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
529873
23.4%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.6%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.9%
d 105714
 
4.7%
t 100258
 
4.4%
Other values (82) 600381
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
529873
23.4%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.6%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.9%
d 105714
 
4.7%
t 100258
 
4.4%
Other values (82) 600381
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
529873
23.4%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.6%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.9%
d 105714
 
4.7%
t 100258
 
4.4%
Other values (82) 600381
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
529873
23.4%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.6%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.9%
d 105714
 
4.7%
t 100258
 
4.4%
Other values (82) 600381
26.6%

host_id
Real number (ℝ)

Distinct21980
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.784179 × 108
Minimum1671
Maximum5.5271448 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:34.151121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1671
5-th percentile2981903.8
Q124167859
median96445730
Q33.2560925 × 108
95-th percentile5.0692743 × 108
Maximum5.5271448 × 108
Range5.5271281 × 108
Interquartile range (IQR)3.0144139 × 108

Descriptive statistics

Standard deviation1.7696843 × 108
Coefficient of variation (CV)0.99187597
Kurtosis-0.90013792
Mean1.784179 × 108
Median Absolute Deviation (MAD)86663858
Skewness0.75158178
Sum6.4244719 × 1012
Variance3.1317825 × 1016
MonotonicityNot monotonic
2024-04-27T12:58:34.562581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000862 185
 
0.5%
91654021 157
 
0.4%
341887136 145
 
0.4%
1982737 142
 
0.4%
47584281 142
 
0.4%
371026651 128
 
0.4%
14315601 85
 
0.2%
74463624 80
 
0.2%
30165706 77
 
0.2%
12909867 69
 
0.2%
Other values (21970) 34798
96.6%
ValueCountFrequency (%)
1671 1
 
< 0.1%
3607 1
 
< 0.1%
11739 9
< 0.1%
19065 3
 
< 0.1%
34105 2
 
< 0.1%
37072 1
 
< 0.1%
48024 7
< 0.1%
60098 1
 
< 0.1%
64036 2
 
< 0.1%
68997 1
 
< 0.1%
ValueCountFrequency (%)
552714482 1
< 0.1%
552420950 1
< 0.1%
552339316 1
< 0.1%
552326533 1
< 0.1%
552283430 1
< 0.1%
552282629 1
< 0.1%
552267363 1
< 0.1%
552264669 1
< 0.1%
552258899 1
< 0.1%
552258099 1
< 0.1%
Distinct6268
Distinct (%)17.4%
Missing9
Missing (%)< 0.1%
Memory size281.4 KiB
2024-04-27T12:58:35.246225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length32
Mean length7.3657602
Min length1

Characters and Unicode

Total characters265160
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3526 ?
Unique (%)9.8%

Sample

1st rowZeilma , Da
2nd rowPriscila
3rd rowRachel
4th rowMaria
5th rowKatiuscia
ValueCountFrequency (%)
maria 894
 
2.1%
ana 581
 
1.3%
rio 516
 
1.2%
carlos 493
 
1.1%
luiz 382
 
0.9%
daniel 359
 
0.8%
rodrigo 336
 
0.8%
ricardo 332
 
0.8%
pedro 331
 
0.8%
rafael 319
 
0.7%
Other values (5070) 38787
89.5%
2024-04-27T12:58:36.245701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36008
Missing (%)100.0%
Memory size281.4 KiB
Distinct156
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:36.872085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length22
Mean length10.281743
Min length3

Characters and Unicode

Total characters370225
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowFlamengo
2nd rowSanta Teresa
3rd rowCopacabana
4th rowJardim Botânico
5th rowBarra da Tijuca
ValueCountFrequency (%)
copacabana 10982
21.7%
tijuca 3953
 
7.8%
barra 3653
 
7.2%
da 3639
 
7.2%
ipanema 3455
 
6.8%
jacarepaguá 1813
 
3.6%
recreio 1804
 
3.6%
dos 1804
 
3.6%
bandeirantes 1804
 
3.6%
leblon 1742
 
3.4%
Other values (186) 16035
31.6%
2024-04-27T12:58:37.987131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct18712
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.96758
Minimum-23.073276
Maximum-22.74969
Zeros0
Zeros (%)0.0%
Negative36008
Negative (%)100.0%
Memory size281.4 KiB
2024-04-27T12:58:38.463857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-23.073276
5-th percentile-23.014033
Q1-22.984712
median-22.97286
Q3-22.956277
95-th percentile-22.910943
Maximum-22.74969
Range0.323586
Interquartile range (IQR)0.0284345

Descriptive statistics

Standard deviation0.034686657
Coefficient of variation (CV)-0.0015102443
Kurtosis4.1290957
Mean-22.96758
Median Absolute Deviation (MAD)0.01227
Skewness1.2785008
Sum-827016.63
Variance0.0012031642
MonotonicityNot monotonic
2024-04-27T12:58:38.836146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.99804 54
 
0.1%
-22.980897 36
 
0.1%
-22.980449 25
 
0.1%
-23.01137 24
 
0.1%
-22.98456 20
 
0.1%
-22.9643 20
 
0.1%
-22.98525 19
 
0.1%
-22.960804 19
 
0.1%
-22.98361 18
 
< 0.1%
-22.98253 18
 
< 0.1%
Other values (18702) 35755
99.3%
ValueCountFrequency (%)
-23.073276 1
< 0.1%
-23.07305 1
< 0.1%
-23.07284 1
< 0.1%
-23.072737 1
< 0.1%
-23.07262 1
< 0.1%
-23.07241 1
< 0.1%
-23.0722 2
< 0.1%
-23.072154 1
< 0.1%
-23.07215 1
< 0.1%
-23.07211 1
< 0.1%
ValueCountFrequency (%)
-22.74969 1
< 0.1%
-22.74995 1
< 0.1%
-22.75051 1
< 0.1%
-22.75061 1
< 0.1%
-22.750692 1
< 0.1%
-22.75077 1
< 0.1%
-22.75094 1
< 0.1%
-22.75144 1
< 0.1%
-22.75195 1
< 0.1%
-22.75239 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct20869
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.249377
Minimum-43.723009
Maximum-43.1044
Zeros0
Zeros (%)0.0%
Negative36008
Negative (%)100.0%
Memory size281.4 KiB
2024-04-27T12:58:39.200901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-43.723009
5-th percentile-43.471277
Q1-43.306566
median-43.194825
Q3-43.185573
95-th percentile-43.174845
Maximum-43.1044
Range0.618609
Interquartile range (IQR)0.120993

Descriptive statistics

Standard deviation0.099240937
Coefficient of variation (CV)-0.0022946212
Kurtosis1.2008506
Mean-43.249377
Median Absolute Deviation (MAD)0.017098
Skewness-1.4714597
Sum-1557323.5
Variance0.0098487635
MonotonicityNot monotonic
2024-04-27T12:58:39.605766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.256821 58
 
0.2%
-43.422441 40
 
0.1%
-43.42267 25
 
0.1%
-43.190266 23
 
0.1%
-43.19043 19
 
0.1%
-43.19024 19
 
0.1%
-43.19053 19
 
0.1%
-43.19123 19
 
0.1%
-43.1911 18
 
< 0.1%
-43.19089 17
 
< 0.1%
Other values (20859) 35751
99.3%
ValueCountFrequency (%)
-43.723009 1
< 0.1%
-43.71038 1
< 0.1%
-43.701286 1
< 0.1%
-43.701218 1
< 0.1%
-43.70074 1
< 0.1%
-43.699819 1
< 0.1%
-43.699189 1
< 0.1%
-43.69332 1
< 0.1%
-43.69155 1
< 0.1%
-43.690056 1
< 0.1%
ValueCountFrequency (%)
-43.1044 1
< 0.1%
-43.104605 1
< 0.1%
-43.10505 1
< 0.1%
-43.105132 1
< 0.1%
-43.10552 1
< 0.1%
-43.10575 1
< 0.1%
-43.105767 1
< 0.1%
-43.105796 1
< 0.1%
-43.10593 1
< 0.1%
-43.10605 1
< 0.1%

room_type
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
Entire home/apt
28468 
Private room
6910 
Shared room
 
592
Hotel room
 
38

Length

Max length15
Median length15
Mean length14.353255
Min length10

Characters and Unicode

Total characters516832
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 28468
79.1%
Private room 6910
 
19.2%
Shared room 592
 
1.6%
Hotel room 38
 
0.1%

Length

2024-04-27T12:58:40.020917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T12:58:40.381980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 28468
39.5%
home/apt 28468
39.5%
room 7540
 
10.5%
private 6910
 
9.6%
shared 592
 
0.8%
hotel 38
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

price
Real number (ℝ)

MISSING  SKEWED 

Distinct3230
Distinct (%)9.4%
Missing1771
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean1211.7196
Minimum0
Maximum552637
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:40.875335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150
Q1361
median660
Q31114
95-th percentile3135.4
Maximum552637
Range552637
Interquartile range (IQR)753

Descriptive statistics

Standard deviation5790.9374
Coefficient of variation (CV)4.7791069
Kurtosis5727.864
Mean1211.7196
Median Absolute Deviation (MAD)340
Skewness66.967076
Sum41485643
Variance33534956
MonotonicityNot monotonic
2024-04-27T12:58:41.261972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 538
 
1.5%
500 498
 
1.4%
300 398
 
1.1%
800 389
 
1.1%
600 382
 
1.1%
400 374
 
1.0%
350 328
 
0.9%
1500 321
 
0.9%
200 311
 
0.9%
250 304
 
0.8%
Other values (3220) 30394
84.4%
(Missing) 1771
 
4.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
26 3
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
39 1
 
< 0.1%
40 2
< 0.1%
41 1
 
< 0.1%
42 2
< 0.1%
43 2
< 0.1%
ValueCountFrequency (%)
552637 1
 
< 0.1%
500000 2
 
< 0.1%
214786 1
 
< 0.1%
189982 1
 
< 0.1%
110130 1
 
< 0.1%
100118 1
 
< 0.1%
100000 5
< 0.1%
99000 1
 
< 0.1%
80080 1
 
< 0.1%
69157 1
 
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4193513
Minimum1
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:41.768232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum1125
Range1124
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.738877
Coefficient of variation (CV)5.1452974
Kurtosis1033.295
Mean4.4193513
Median Absolute Deviation (MAD)1
Skewness28.220927
Sum159132
Variance517.05651
MonotonicityNot monotonic
2024-04-27T12:58:42.117671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10443
29.0%
1 9243
25.7%
3 7858
21.8%
4 2883
 
8.0%
5 2446
 
6.8%
7 1011
 
2.8%
10 422
 
1.2%
6 374
 
1.0%
30 301
 
0.8%
15 286
 
0.8%
Other values (61) 741
 
2.1%
ValueCountFrequency (%)
1 9243
25.7%
2 10443
29.0%
3 7858
21.8%
4 2883
 
8.0%
5 2446
 
6.8%
6 374
 
1.0%
7 1011
 
2.8%
8 57
 
0.2%
9 14
 
< 0.1%
10 422
 
1.2%
ValueCountFrequency (%)
1125 1
 
< 0.1%
1000 3
 
< 0.1%
999 3
 
< 0.1%
960 1
 
< 0.1%
730 1
 
< 0.1%
720 1
 
< 0.1%
630 1
 
< 0.1%
500 3
 
< 0.1%
365 38
0.1%
362 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct367
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.545545
Minimum0
Maximum638
Zeros10302
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:42.507289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q320
95-th percentile93
Maximum638
Range638
Interquartile range (IQR)20

Descriptive statistics

Standard deviation40.60038
Coefficient of variation (CV)2.0772191
Kurtosis29.974078
Mean19.545545
Median Absolute Deviation (MAD)4
Skewness4.4850733
Sum703796
Variance1648.3908
MonotonicityNot monotonic
2024-04-27T12:58:43.159968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10302
28.6%
1 3233
 
9.0%
2 2282
 
6.3%
3 1641
 
4.6%
4 1308
 
3.6%
5 1103
 
3.1%
6 870
 
2.4%
7 803
 
2.2%
8 658
 
1.8%
9 641
 
1.8%
Other values (357) 13167
36.6%
ValueCountFrequency (%)
0 10302
28.6%
1 3233
 
9.0%
2 2282
 
6.3%
3 1641
 
4.6%
4 1308
 
3.6%
5 1103
 
3.1%
6 870
 
2.4%
7 803
 
2.2%
8 658
 
1.8%
9 641
 
1.8%
ValueCountFrequency (%)
638 1
< 0.1%
627 1
< 0.1%
587 1
< 0.1%
555 1
< 0.1%
552 1
< 0.1%
537 1
< 0.1%
532 1
< 0.1%
504 1
< 0.1%
500 1
< 0.1%
484 1
< 0.1%

last_review
Date

MISSING 

Distinct1424
Distinct (%)5.5%
Missing10302
Missing (%)28.6%
Memory size281.4 KiB
Minimum2012-02-21 00:00:00
Maximum2023-12-29 00:00:00
2024-04-27T12:58:43.522055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:44.023464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct678
Distinct (%)2.6%
Missing10302
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean1.0462771
Minimum0.01
Maximum13.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:44.627323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.2
median0.67
Q31.5
95-th percentile3.33
Maximum13.75
Range13.74
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1578162
Coefficient of variation (CV)1.1066056
Kurtosis8.2957752
Mean1.0462771
Median Absolute Deviation (MAD)0.53
Skewness2.215832
Sum26895.6
Variance1.3405383
MonotonicityNot monotonic
2024-04-27T12:58:45.165223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 623
 
1.7%
0.06 563
 
1.6%
0.08 494
 
1.4%
0.19 481
 
1.3%
0.04 453
 
1.3%
0.02 440
 
1.2%
0.13 396
 
1.1%
0.25 370
 
1.0%
1 354
 
1.0%
0.17 331
 
0.9%
Other values (668) 21201
58.9%
(Missing) 10302
28.6%
ValueCountFrequency (%)
0.01 234
 
0.6%
0.02 440
1.2%
0.03 296
0.8%
0.04 453
1.3%
0.05 205
 
0.6%
0.06 563
1.6%
0.07 253
0.7%
0.08 494
1.4%
0.09 254
0.7%
0.1 623
1.7%
ValueCountFrequency (%)
13.75 2
< 0.1%
13.52 1
< 0.1%
11.6 1
< 0.1%
11.41 1
< 0.1%
11.32 1
< 0.1%
11.25 1
< 0.1%
11.03 1
< 0.1%
10.54 1
< 0.1%
10.32 1
< 0.1%
10 1
< 0.1%
Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4932237
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:45.607603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile43
Maximum185
Range184
Interquartile range (IQR)4

Descriptive statistics

Standard deviation25.677082
Coefficient of variation (CV)2.70478
Kurtosis23.911237
Mean9.4932237
Median Absolute Deviation (MAD)1
Skewness4.7436407
Sum341832
Variance659.31253
MonotonicityNot monotonic
2024-04-27T12:58:46.025025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17249
47.9%
2 5372
 
14.9%
3 2535
 
7.0%
4 1624
 
4.5%
5 1000
 
2.8%
6 924
 
2.6%
7 581
 
1.6%
8 504
 
1.4%
9 432
 
1.2%
12 372
 
1.0%
Other values (48) 5415
 
15.0%
ValueCountFrequency (%)
1 17249
47.9%
2 5372
 
14.9%
3 2535
 
7.0%
4 1624
 
4.5%
5 1000
 
2.8%
6 924
 
2.6%
7 581
 
1.6%
8 504
 
1.4%
9 432
 
1.2%
10 330
 
0.9%
ValueCountFrequency (%)
185 185
0.5%
157 157
0.4%
145 145
0.4%
142 284
0.8%
128 128
0.4%
85 85
 
0.2%
80 80
 
0.2%
77 77
 
0.2%
69 69
 
0.2%
57 57
 
0.2%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.41891
Minimum0
Maximum365
Zeros5475
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:46.386469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143
median160
Q3316
95-th percentile364
Maximum365
Range365
Interquartile range (IQR)273

Descriptive statistics

Standard deviation135.55391
Coefficient of variation (CV)0.78618933
Kurtosis-1.535679
Mean172.41891
Median Absolute Deviation (MAD)132
Skewness0.12206185
Sum6208460
Variance18374.861
MonotonicityNot monotonic
2024-04-27T12:58:46.783195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5475
 
15.2%
365 1553
 
4.3%
364 643
 
1.8%
269 498
 
1.4%
358 430
 
1.2%
1 404
 
1.1%
363 396
 
1.1%
89 341
 
0.9%
362 341
 
0.9%
360 330
 
0.9%
Other values (356) 25597
71.1%
ValueCountFrequency (%)
0 5475
15.2%
1 404
 
1.1%
2 90
 
0.2%
3 95
 
0.3%
4 85
 
0.2%
5 74
 
0.2%
6 92
 
0.3%
7 80
 
0.2%
8 72
 
0.2%
9 73
 
0.2%
ValueCountFrequency (%)
365 1553
4.3%
364 643
1.8%
363 396
 
1.1%
362 341
 
0.9%
361 238
 
0.7%
360 330
 
0.9%
359 277
 
0.8%
358 430
 
1.2%
357 227
 
0.6%
356 231
 
0.6%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1173073
Minimum0
Maximum124
Zeros14214
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-27T12:58:47.150080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile32
Maximum124
Range124
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.662121
Coefficient of variation (CV)1.638558
Kurtosis8.0016977
Mean7.1173073
Median Absolute Deviation (MAD)2
Skewness2.4872391
Sum256280
Variance136.00507
MonotonicityNot monotonic
2024-04-27T12:58:47.555184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14214
39.5%
1 3431
 
9.5%
2 2400
 
6.7%
3 1721
 
4.8%
4 1282
 
3.6%
5 1074
 
3.0%
6 867
 
2.4%
7 765
 
2.1%
8 693
 
1.9%
9 654
 
1.8%
Other values (89) 8907
24.7%
ValueCountFrequency (%)
0 14214
39.5%
1 3431
 
9.5%
2 2400
 
6.7%
3 1721
 
4.8%
4 1282
 
3.6%
5 1074
 
3.0%
6 867
 
2.4%
7 765
 
2.1%
8 693
 
1.9%
9 654
 
1.8%
ValueCountFrequency (%)
124 1
< 0.1%
120 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
108 2
< 0.1%
105 2
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
94 1
< 0.1%
93 2
< 0.1%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36008
Missing (%)100.0%
Memory size281.4 KiB

Interactions

2024-04-27T12:58:24.069309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:48.078447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:51.292984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:54.558254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:58.332645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:01.665162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:05.287235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:10.064921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:13.601577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:17.115596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:20.723055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:24.438749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:48.481254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:51.546090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:54.851798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:58.731165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:01.978196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:05.657014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:10.321512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:13.862641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:17.425463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:20.986882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:24.716464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:48.756663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:51.926986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:55.261809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:59.067207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:02.344839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:05.960942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:10.571007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:14.107039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:17.672108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:21.241160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:25.311679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:49.090219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:52.201662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:55.672120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:59.454485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:02.700664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:06.462517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:10.937883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:14.527555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:17.982389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:21.519027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:25.596329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:49.388556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:52.569040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:56.079808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:59.724447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:03.373027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:07.094918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:11.216468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:14.791160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:18.276696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:21.810911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:25.843390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:49.643589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:52.838836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:56.339906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:59.964015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:03.648517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:07.495526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:11.521600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:15.114606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:18.597598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:22.080586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:26.077390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:49.891190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:53.098366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:56.677133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:00.270344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:03.921841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:07.816837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:11.835833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:15.360620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:18.939512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:22.325715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:26.355240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:50.194634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:53.341188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:56.974176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:00.536822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:04.175794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:08.121439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:12.082416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:15.667196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:19.320183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:22.630526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:26.987971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:50.505363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:53.597833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:57.264855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:00.844774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:04.464889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:08.504014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:12.440434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:15.961573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:19.717430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:22.954727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:27.503432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:50.773121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:53.881734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:57.610871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:01.144236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:04.745299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:08.911577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:12.927045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:16.342761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:20.134092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:23.424656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:27.806286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:51.030075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:54.182137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:57:57.973718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:01.401834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:05.022823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:09.719834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:13.306673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:16.770966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:20.468752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-27T12:58:23.816268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-27T12:58:47.917121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsnumber_of_reviewsnumber_of_reviews_ltmpricereviews_per_monthroom_type
availability_3651.0000.0260.0080.0030.018-0.126-0.093-0.202-0.1820.031-0.1870.076
calculated_host_listings_count0.0261.000-0.1430.105-0.0850.038-0.0770.0540.0780.1070.0610.082
host_id0.008-0.1431.0000.4520.017-0.076-0.181-0.164-0.036-0.1170.1570.053
id0.0030.1050.4521.000-0.0070.017-0.224-0.384-0.111-0.0290.3850.028
latitude0.018-0.0850.017-0.0071.0000.543-0.058-0.026-0.025-0.341-0.0370.110
longitude-0.1260.038-0.0760.0170.5431.0000.0910.1360.141-0.0150.0850.088
minimum_nights-0.093-0.077-0.181-0.224-0.0580.0911.000-0.040-0.0970.156-0.1920.000
number_of_reviews-0.2020.054-0.164-0.384-0.0260.136-0.0401.0000.868-0.0700.5940.041
number_of_reviews_ltm-0.1820.078-0.036-0.111-0.0250.141-0.0970.8681.000-0.0460.8040.067
price0.0310.107-0.117-0.029-0.341-0.0150.156-0.070-0.0461.0000.0130.013
reviews_per_month-0.1870.0610.1570.385-0.0370.085-0.1920.5940.8040.0131.0000.063
room_type0.0760.0820.0530.0280.1100.0880.0000.0410.0670.0130.0631.000

Missing values

2024-04-27T12:58:28.388123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T12:58:29.567327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
0216461Rental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath1154263Zeilma , DaNoneFlamengo-22.93990-43.17676Private room734.010NaTNaN13650None
1328626Rental unit in Rio de Janeiro · 2 bedrooms · 5 beds · 3 baths1675497PriscilaNoneSanta Teresa-22.92286-43.18790Entire home/apt1250.02012012-02-210.0113650None
2220705Rental unit in Rio de Janeiro · 1 bedroom · 2 beds · 2.5 baths1144461RachelNoneCopacabana-22.98246-43.19376Private room300.030NaTNaN200None
3329615Home in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath1416853MariaNoneJardim Botânico-22.96547-43.23666Private room972.010NaTNaN100None
4337345Rental unit in Rio de Janeiro · 1 bedroom · 2 beds · 1 bath1714680KatiusciaNoneBarra da Tijuca-23.01147-43.36394Private room2411.010NaTNaN13650None
5247052Rental unit in Rio de Janeiro · 2 bedrooms · 2 beds · 3 baths1295841OsmarNoneBotafogo-22.95665-43.18481Entire home/apt734.050NaTNaN130None
6346694Rental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath1612576JassananNoneVila da Penha-22.84208-43.31572Private room313.010NaTNaN13620None
7352718Rental unit in Rio de Janeiro · 1 bedroom · 1 bed · 3 baths1786148RicardoNoneCopacabana-22.98127-43.19215Private room500.0122013-02-140.0113570None
8355075Rental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1.5 baths1707318AlessandroNoneFlamengo-22.93527-43.17834Private room978.010NaTNaN13650None
986978Rental unit in Rio de Janeiro · 1 bedroom · 2 beds · 2 baths476838AntonioNoneJacarepaguá-22.97293-43.38321Private room723.010NaTNaN13650None
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
359981053711355674934096Rental unit in Rio de Janeiro · New · 1 bedroom · 1 bath535697473VictorNoneCopacabana-22.967333-43.181577Entire home/apt487.010NaTNaN22660None
359991053716797020259779Rental unit in Rio de Janeiro · New · 1 bedroom · 2 beds · 1 bath154114238BrenoNoneBarra da Tijuca-23.010436-43.357412Entire home/apt889.0222023-12-272.04552None
360001053720091301615651Rental unit in Rio de Janeiro · New · 1 bedroom · 1 bath55209037DiogoNoneCamorim-22.980449-43.422670Entire home/apt229.010NaTNaN12670None
360011053736263866696090Rental unit in Rio de Janeiro · New · 1 bedroom · 2 beds · 1 bath13411812FernandoNoneCopacabana-22.969913-43.188015Entire home/apt760.020NaTNaN16870None
360021053743193407023650Rental unit in Rio de Janeiro · New · 1 bedroom · 2 beds · 1 bath113031534JoãoNoneBarra da Tijuca-23.003550-43.340740Entire home/apt600.010NaTNaN22640None
360031053746406056980189Home in Rio de Janeiro · New · 1 bedroom · 4 beds · 1 bath470173166MarcoNoneBotafogo-22.956393-43.182119Private room200.020NaTNaN8900None
360041053756202332288557Rental unit in Rio de Janeiro · New · 1 bedroom · 2 beds · 1 bath6000862Omar Do RioNoneCopacabana-22.980547-43.195863Entire home/apt727.020NaTNaN1853580None
360051053789340172837654Rental unit in Rio de Janeiro · New · 1 bedroom · 1 bed · 1 bath206898000PabloNoneCopacabana-22.968340-43.185855Private room743.010NaTNaN12700None
360061053808194231554793Rental unit in Rio de Janeiro · New · 1 bedroom · 1 bath536983374IzaiasNoneVidigal-22.996260-43.240830Entire home/apt560.010NaTNaN12490None
360071053823261878675052Rental unit in Rio de Janeiro · New · 1 bedroom · 1 bed · 1 bath694816SamuelNoneGlória-22.912241-43.171288Private room175.020NaTNaN1190None